Mapping with ggplot

Author

Meddy Rutayisire

Class was inspired by works by Mel Moreno and Mathieu Basile found Here

Introduction to Mapping in R

Overview

Welcome to the “Introduction to Mapping in R” class! This course is designed to provide you with the foundational skills needed to create and visualize geographic data using R.

R is a powerful statistical programming language that offers extensive libraries and tools for working with spatial data, making it an excellent choice for mapping and geographic information system (GIS) applications.

Objectives

By the end of this course, you will be able to: - Understand the basics of spatial data and geographic coordinate systems.

Import and manipulate spatial data in R. Create a variety of maps using popular R packages. Customize maps to highlight specific data insights.

Getting Started

pacman::p_load(
 tidyverse,sf , rnaturalearth, here
)

Set Theme

theme_set(theme_bw())

The package rnaturalearth provides a map of countries of the entire world. Use ne_countries to pull country data and choose the scale (rnaturalearthhires is necessary for scale = “large”). The function can return sp classes (default) or directly sf classes, as defined in the argument returnclass:

world_df <- ne_countries(scale = "medium", returnclass = "sf")

class(world_df)

[1] "sf"         "data.frame"

sample(names(world_df),10)

 [1] "woe_id_eh"  "fclass_id"  "labelrank"  "fclass_eg"  "name_tr"   
 [6] "tiny"       "name_pl"    "level"      "fclass_es"  "adm0_a3_il"

Data and basic plot (ggplot and geom_sf)

ggplot(data = world_df) +
    geom_sf()

Add labels

ggplot(data = world_df) +
    geom_sf() +
    xlab("Longitude") + ylab("Latitude") +
    labs(title = "World map")

Add Color

ggplot(data = world_df) + 
    geom_sf(color = "black", fill = "lightgreen")

Filling colors based on a variable

ggplot(data = world_df) +
    geom_sf(aes(fill = pop_est))

Customising to a color palette - Viridis

ggplot(data = world_df) +
    geom_sf(aes(fill = pop_est)) +
    scale_fill_viridis_c(option = "mako", trans = "sqrt")

Customising to a color palette - Colorbrewer

ggplot(data = world_df) +
    geom_sf(aes(fill = pop_est)) +
    scale_fill_distiller(palette = 2)

Filtering to Africa

world_df |> 
  filter(continent == "Africa") |> 
  ggplot() +
  geom_sf(aes(fill = pop_est)) +
  scale_fill_viridis_c(name = "Population (millions)", 
                       breaks = c(1e6, 10e6, 20e6, 50e6, 100e6, 200e6),
                       labels = c("1M", "10M", "20M", "50M", "100M", "200M"),
                       option = "viridis") +
  labs(title = "Map of Africa Showing Distribution of Populations") +
  theme_void() +
  theme(legend.position = "bottom")

Explore several filters Views and Facetting

world_df |> 
  filter(continent %in% c("Africa", "Europe")) |> 
  ggplot() +
  geom_sf()

Ugandas Case Study

Load data

uganda_sp <- st_read(here("Shape Files/Districts and Cities/uganda_districts_2019-wgs84.shp"))

Reading layer `uganda_districts_2019-wgs84' from data source 
  `/Users/rutayisiremeddy/Documents/R classes/R-Classes/Mapping/Shape Files/Districts and Cities/uganda_districts_2019-wgs84.shp' 
  using driver `ESRI Shapefile'
Simple feature collection with 136 features and 9 fields
Geometry type: MULTIPOLYGON
Dimension:     XY
Bounding box:  xmin: 29.57216 ymin: -1.481474 xmax: 35.00105 ymax: 4.231367
Geodetic CRS:  WGS 84

Plot basic Map

ggplot(data = uganda_sp)+
  geom_sf()

Add water bodies

water_sp <- st_read(here("Shape Files/Water Bodies/UGA_water_areas_dcw.shp"))

Reading layer `UGA_water_areas_dcw' from data source 
  `/Users/rutayisiremeddy/Documents/R classes/R-Classes/Mapping/Shape Files/Water Bodies/UGA_water_areas_dcw.shp' 
  using driver `ESRI Shapefile'
Simple feature collection with 86 features and 5 fields
Geometry type: POLYGON
Dimension:     XY
Bounding box:  xmin: 29.64843 ymin: -1.398267 xmax: 34.23037 ymax: 3.436696
Geodetic CRS:  WGS 84

 ggplot(data = uganda_sp)+
   geom_sf()+
   geom_sf(data = water_sp)

Color the Water Bodies and Basic Styling

ggplot(data = uganda_sp)+
  geom_sf()+
  geom_sf(data = water_sp,fill = "lightblue")+
  theme_void()+
  labs(title = "Map of the Uganda", caption =  "DataSource: Open_Data_Uganda")